Per-movements comparison. Events are highly coupled with the dynamic of the scene. If parts of the body are static, fewer events are recorded. As a consequence, spatial sparsity increases and makes prediction tasks more challenging. To evaluate the impact of static body parts on our approach, we propose a per-movements study for our ImageNet-pretrained method. Table REF  compares our constant-count and spatio-temporal voxel-grid approaches with DHP19 [1]} event-based stereo approach. Differently from [1]}, our approach is based upon the more recent state of the art solutions [3]}, [4]} and reaches a higher per-movement accuracy and lower per-movement standard deviation. As expected, performance decreases when subjects perform movements with only parts of the body (e.g., Punch up forwards left implies static legs). This drop in performance matches the results of the stereo-vision approach (e.g., Punch forwards left/right). On the other hand, we notice above average performances for movements that involve the whole body, such as knee lift and hand movements (during these movements, subjects move on the spot and the whole body generates events).
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